{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a25fe874-687c-4843-87ca-5253efc17ebb",
   "metadata": {},
   "source": [
    "# Preprocessing the scRNA-seq data\n",
    "\n",
    "In this tutorial we will perform some very basic preprocessing steps."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11702c47-2afc-4b6b-8824-823122a3df71",
   "metadata": {},
   "source": [
    "## Download data\n",
    "\n",
    "The data used for this tutorial is freely available, and can be downloaded from the [10x genomics website](https://www.10xgenomics.com/datasets/frozen-human-healthy-brain-tissue-3-k-1-standard-1-0-0)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "80609ad9-2f09-44b7-88b0-0d5392f26a7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.chdir(\"/staging/leuven/stg_00002/lcb/sdewin/PhD/python_modules/scenicplus_development_tutorial/scRNA_seq_pp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d1828000-1ad0-4845-9e8b-29cb2d8fa830",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2024-03-06 13:46:50--  https://cf.10xgenomics.com/samples/cell-arc/2.0.0/human_brain_3k/human_brain_3k_filtered_feature_bc_matrix.tar.gz\n",
      "Resolving cf.10xgenomics.com (cf.10xgenomics.com)... 2606:4700::6812:ad, 2606:4700::6812:1ad, 104.18.0.173, ...\n",
      "Connecting to cf.10xgenomics.com (cf.10xgenomics.com)|2606:4700::6812:ad|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 170112442 (162M) [application/x-tar]\n",
      "Saving to: ‘data/filtered_feature_bc_matrix.tar.gz’\n",
      "\n",
      "data/filtered_featu 100%[===================>] 162.23M  28.5MB/s    in 8.5s    \n",
      "\n",
      "2024-03-06 13:47:00 (19.1 MB/s) - ‘data/filtered_feature_bc_matrix.tar.gz’ saved [170112442/170112442]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!mkdir -p data\n",
    "!wget -O data/filtered_feature_bc_matrix.tar.gz https://cf.10xgenomics.com/samples/cell-arc/2.0.0/human_brain_3k/human_brain_3k_filtered_feature_bc_matrix.tar.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2f559574-1fe0-483d-ac07-ebd03b528bac",
   "metadata": {},
   "outputs": [],
   "source": [
    "!cd data; tar -xzf filtered_feature_bc_matrix.tar.gz; cd .."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "a6369438-1d9e-4f8e-bbe4-cfa5548baa13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2024-03-06 14:07:48--  https://raw.githubusercontent.com/aertslab/pycisTopic/polars/data/cell_data_human_cerebellum.tsv\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 2606:50c0:8001::154, 2606:50c0:8002::154, 2606:50c0:8003::154, ...\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|2606:50c0:8001::154|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 309204 (302K) [text/plain]\n",
      "Saving to: ‘data/cell_data.tsv’\n",
      "\n",
      "data/cell_data.tsv  100%[===================>] 301.96K  --.-KB/s    in 0.04s   \n",
      "\n",
      "2024-03-06 14:07:48 (7.38 MB/s) - ‘data/cell_data.tsv’ saved [309204/309204]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget -O data/cell_data.tsv https://raw.githubusercontent.com/aertslab/pycisTopic/polars/data/cell_data_human_cerebellum.tsv"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29535482-38bd-43f1-ab7c-0d631b4010f4",
   "metadata": {},
   "source": [
    "## Preprocessing\n",
    "\n",
    "We will do some very basic preprocessing steps, for more information we refer the reader to the [Scanpy tutorials](https://scanpy.readthedocs.io/en/stable/tutorials.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4ebcad71-7b51-4eb0-8d20-46a2125ec507",
   "metadata": {},
   "outputs": [],
   "source": [
    "import scanpy as sc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "3a43a2ad-e309-4edf-93a4-13b88821b4a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata = sc.read_10x_mtx(\n",
    "    \"data/filtered_feature_bc_matrix/\",\n",
    "    var_names = \"gene_symbols\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "2b37ee71-f533-468b-86d9-dde6e9aaf80e",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata.var_names_make_unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "2aa0c4d3-9e27-406c-b156-c314262a6a41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 3233 × 36601\n",
       "    var: 'gene_ids', 'feature_types'"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "267b92bb-02a4-4d53-85c6-caaf23318657",
   "metadata": {},
   "source": [
    "We have already annotated this dataset, this is beyond the scope of this tutorial. We will load this annotation here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "82137ec7-b878-4b9e-9e64-2a2bec78229c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VSN_cell_type</th>\n",
       "      <th>VSN_leiden_res0.3</th>\n",
       "      <th>VSN_leiden_res0.6</th>\n",
       "      <th>VSN_leiden_res0.9</th>\n",
       "      <th>VSN_leiden_res1.2</th>\n",
       "      <th>VSN_sample_id</th>\n",
       "      <th>Seurat_leiden_res0.6</th>\n",
       "      <th>Seurat_leiden_res1.2</th>\n",
       "      <th>Seurat_cell_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AAACAGCCATTATGCG-1-10x_multiome_brain</th>\n",
       "      <td>MOL_B</td>\n",
       "      <td>MOL_B (0)</td>\n",
       "      <td>MOL_B_1 (0)</td>\n",
       "      <td>MOL_B_1  (1)</td>\n",
       "      <td>MOL_B_3 (6)</td>\n",
       "      <td>10x_multiome_brain</td>\n",
       "      <td>NFOL (1)</td>\n",
       "      <td>MOL (1)</td>\n",
       "      <td>MOL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACCAACATAGACCC-1-10x_multiome_brain</th>\n",
       "      <td>MOL_B</td>\n",
       "      <td>MOL_B (0)</td>\n",
       "      <td>MOL_B_1 (0)</td>\n",
       "      <td>MOL_B_3 (5)</td>\n",
       "      <td>MOL_B_4 (4)</td>\n",
       "      <td>10x_multiome_brain</td>\n",
       "      <td>NFOL (1)</td>\n",
       "      <td>NFOL (3)</td>\n",
       "      <td>NFOL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACCGAAGATGCCTG-1-10x_multiome_brain</th>\n",
       "      <td>INH_VIP</td>\n",
       "      <td>INH_VIP (6)</td>\n",
       "      <td>INH_VIP (8)</td>\n",
       "      <td>INH_VIP (8)</td>\n",
       "      <td>INH_VIP (10)</td>\n",
       "      <td>10x_multiome_brain</td>\n",
       "      <td>INH_VIP (7)</td>\n",
       "      <td>INH_VIP (6)</td>\n",
       "      <td>INH_VIP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACCGAAGTTAGCTA-1-10x_multiome_brain</th>\n",
       "      <td>MOL_A</td>\n",
       "      <td>MOL_A (1)</td>\n",
       "      <td>MOL_A_2 (1)</td>\n",
       "      <td>MOL_A_1  (0)</td>\n",
       "      <td>MOL_A_2 (0)</td>\n",
       "      <td>10x_multiome_brain</td>\n",
       "      <td>NFOL (1)</td>\n",
       "      <td>NFOL (3)</td>\n",
       "      <td>NFOL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAACCGCGTTAGCCAA-1-10x_multiome_brain</th>\n",
       "      <td>MGL</td>\n",
       "      <td>MGL (7)</td>\n",
       "      <td>MGL (10)</td>\n",
       "      <td>MGL (10)</td>\n",
       "      <td>MGL (12)</td>\n",
       "      <td>10x_multiome_brain</td>\n",
       "      <td>MGL (8)</td>\n",
       "      <td>MGL (9)</td>\n",
       "      <td>MGL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTGTGAAGGGTGAGT-1-10x_multiome_brain</th>\n",
       "      <td>INH_VIP</td>\n",
       "      <td>INH_VIP (6)</td>\n",
       "      <td>INH_VIP (8)</td>\n",
       "      <td>INH_VIP (8)</td>\n",
       "      <td>INH_VIP (10)</td>\n",
       "      <td>10x_multiome_brain</td>\n",
       "      <td>INH_SST (5)</td>\n",
       "      <td>INH_SST (8)</td>\n",
       "      <td>INH_SST</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTGTGAAGTCAGGCC-1-10x_multiome_brain</th>\n",
       "      <td>AST_CER</td>\n",
       "      <td>AST_CER (2)</td>\n",
       "      <td>AST_CER (2)</td>\n",
       "      <td>AST_CER (2)</td>\n",
       "      <td>AST_CER_1 (7)</td>\n",
       "      <td>10x_multiome_brain</td>\n",
       "      <td>BG (2)</td>\n",
       "      <td>BG (2)</td>\n",
       "      <td>BG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTGTGGCATGCTTAG-1-10x_multiome_brain</th>\n",
       "      <td>MOL_B</td>\n",
       "      <td>MOL_B (0)</td>\n",
       "      <td>MOL_B_1 (0)</td>\n",
       "      <td>MOL_B_1  (1)</td>\n",
       "      <td>MOL_B_1 (1)</td>\n",
       "      <td>10x_multiome_brain</td>\n",
       "      <td>MOL (0)</td>\n",
       "      <td>MOL (1)</td>\n",
       "      <td>MOL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTGTTGGTGATCAGC-1-10x_multiome_brain</th>\n",
       "      <td>MOL_A</td>\n",
       "      <td>MOL_A (1)</td>\n",
       "      <td>MOL_A_2 (1)</td>\n",
       "      <td>MOL_A_1  (0)</td>\n",
       "      <td>MOL_A_1 (11)</td>\n",
       "      <td>10x_multiome_brain</td>\n",
       "      <td>NFOL (1)</td>\n",
       "      <td>NFOL (3)</td>\n",
       "      <td>NFOL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTGTTGGTGATTTGG-1-10x_multiome_brain</th>\n",
       "      <td>INH_SST</td>\n",
       "      <td>INH_SST (5)</td>\n",
       "      <td>INH_SST (7)</td>\n",
       "      <td>INH_SST (7)</td>\n",
       "      <td>INH_SST (9)</td>\n",
       "      <td>10x_multiome_brain</td>\n",
       "      <td>INH_SST (5)</td>\n",
       "      <td>INH_SST (8)</td>\n",
       "      <td>INH_SST</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2392 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      VSN_cell_type VSN_leiden_res0.3  \\\n",
       "AAACAGCCATTATGCG-1-10x_multiome_brain         MOL_B         MOL_B (0)   \n",
       "AAACCAACATAGACCC-1-10x_multiome_brain         MOL_B         MOL_B (0)   \n",
       "AAACCGAAGATGCCTG-1-10x_multiome_brain       INH_VIP       INH_VIP (6)   \n",
       "AAACCGAAGTTAGCTA-1-10x_multiome_brain         MOL_A         MOL_A (1)   \n",
       "AAACCGCGTTAGCCAA-1-10x_multiome_brain           MGL           MGL (7)   \n",
       "...                                             ...               ...   \n",
       "TTTGTGAAGGGTGAGT-1-10x_multiome_brain       INH_VIP       INH_VIP (6)   \n",
       "TTTGTGAAGTCAGGCC-1-10x_multiome_brain       AST_CER       AST_CER (2)   \n",
       "TTTGTGGCATGCTTAG-1-10x_multiome_brain         MOL_B         MOL_B (0)   \n",
       "TTTGTTGGTGATCAGC-1-10x_multiome_brain         MOL_A         MOL_A (1)   \n",
       "TTTGTTGGTGATTTGG-1-10x_multiome_brain       INH_SST       INH_SST (5)   \n",
       "\n",
       "                                      VSN_leiden_res0.6 VSN_leiden_res0.9  \\\n",
       "AAACAGCCATTATGCG-1-10x_multiome_brain       MOL_B_1 (0)      MOL_B_1  (1)   \n",
       "AAACCAACATAGACCC-1-10x_multiome_brain       MOL_B_1 (0)       MOL_B_3 (5)   \n",
       "AAACCGAAGATGCCTG-1-10x_multiome_brain       INH_VIP (8)       INH_VIP (8)   \n",
       "AAACCGAAGTTAGCTA-1-10x_multiome_brain       MOL_A_2 (1)      MOL_A_1  (0)   \n",
       "AAACCGCGTTAGCCAA-1-10x_multiome_brain          MGL (10)          MGL (10)   \n",
       "...                                                 ...               ...   \n",
       "TTTGTGAAGGGTGAGT-1-10x_multiome_brain       INH_VIP (8)       INH_VIP (8)   \n",
       "TTTGTGAAGTCAGGCC-1-10x_multiome_brain       AST_CER (2)       AST_CER (2)   \n",
       "TTTGTGGCATGCTTAG-1-10x_multiome_brain       MOL_B_1 (0)      MOL_B_1  (1)   \n",
       "TTTGTTGGTGATCAGC-1-10x_multiome_brain       MOL_A_2 (1)      MOL_A_1  (0)   \n",
       "TTTGTTGGTGATTTGG-1-10x_multiome_brain       INH_SST (7)       INH_SST (7)   \n",
       "\n",
       "                                      VSN_leiden_res1.2       VSN_sample_id  \\\n",
       "AAACAGCCATTATGCG-1-10x_multiome_brain       MOL_B_3 (6)  10x_multiome_brain   \n",
       "AAACCAACATAGACCC-1-10x_multiome_brain       MOL_B_4 (4)  10x_multiome_brain   \n",
       "AAACCGAAGATGCCTG-1-10x_multiome_brain      INH_VIP (10)  10x_multiome_brain   \n",
       "AAACCGAAGTTAGCTA-1-10x_multiome_brain       MOL_A_2 (0)  10x_multiome_brain   \n",
       "AAACCGCGTTAGCCAA-1-10x_multiome_brain          MGL (12)  10x_multiome_brain   \n",
       "...                                                 ...                 ...   \n",
       "TTTGTGAAGGGTGAGT-1-10x_multiome_brain      INH_VIP (10)  10x_multiome_brain   \n",
       "TTTGTGAAGTCAGGCC-1-10x_multiome_brain     AST_CER_1 (7)  10x_multiome_brain   \n",
       "TTTGTGGCATGCTTAG-1-10x_multiome_brain       MOL_B_1 (1)  10x_multiome_brain   \n",
       "TTTGTTGGTGATCAGC-1-10x_multiome_brain      MOL_A_1 (11)  10x_multiome_brain   \n",
       "TTTGTTGGTGATTTGG-1-10x_multiome_brain       INH_SST (9)  10x_multiome_brain   \n",
       "\n",
       "                                      Seurat_leiden_res0.6  \\\n",
       "AAACAGCCATTATGCG-1-10x_multiome_brain             NFOL (1)   \n",
       "AAACCAACATAGACCC-1-10x_multiome_brain             NFOL (1)   \n",
       "AAACCGAAGATGCCTG-1-10x_multiome_brain          INH_VIP (7)   \n",
       "AAACCGAAGTTAGCTA-1-10x_multiome_brain             NFOL (1)   \n",
       "AAACCGCGTTAGCCAA-1-10x_multiome_brain              MGL (8)   \n",
       "...                                                    ...   \n",
       "TTTGTGAAGGGTGAGT-1-10x_multiome_brain          INH_SST (5)   \n",
       "TTTGTGAAGTCAGGCC-1-10x_multiome_brain               BG (2)   \n",
       "TTTGTGGCATGCTTAG-1-10x_multiome_brain              MOL (0)   \n",
       "TTTGTTGGTGATCAGC-1-10x_multiome_brain             NFOL (1)   \n",
       "TTTGTTGGTGATTTGG-1-10x_multiome_brain          INH_SST (5)   \n",
       "\n",
       "                                      Seurat_leiden_res1.2 Seurat_cell_type  \n",
       "AAACAGCCATTATGCG-1-10x_multiome_brain              MOL (1)              MOL  \n",
       "AAACCAACATAGACCC-1-10x_multiome_brain             NFOL (3)             NFOL  \n",
       "AAACCGAAGATGCCTG-1-10x_multiome_brain          INH_VIP (6)          INH_VIP  \n",
       "AAACCGAAGTTAGCTA-1-10x_multiome_brain             NFOL (3)             NFOL  \n",
       "AAACCGCGTTAGCCAA-1-10x_multiome_brain              MGL (9)              MGL  \n",
       "...                                                    ...              ...  \n",
       "TTTGTGAAGGGTGAGT-1-10x_multiome_brain          INH_SST (8)          INH_SST  \n",
       "TTTGTGAAGTCAGGCC-1-10x_multiome_brain               BG (2)               BG  \n",
       "TTTGTGGCATGCTTAG-1-10x_multiome_brain              MOL (1)              MOL  \n",
       "TTTGTTGGTGATCAGC-1-10x_multiome_brain             NFOL (3)             NFOL  \n",
       "TTTGTTGGTGATTTGG-1-10x_multiome_brain          INH_SST (8)          INH_SST  \n",
       "\n",
       "[2392 rows x 9 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "cell_data = pd.read_table(\"data/cell_data.tsv\", index_col = 0)\n",
    "cell_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a80eeb6-321c-4aa6-9752-9b40bf7045ac",
   "metadata": {},
   "source": [
    "We modify the index of this cell type annotation dataframe so that the cell barcode names match with those in the AnnData object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "fb42c55f-83ae-48f2-b7ef-cb325a74c538",
   "metadata": {},
   "outputs": [],
   "source": [
    "cell_data.index = [cb.rsplit(\"-\", 1)[0] for cb in cell_data.index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "9babb237-8441-40bc-8b29-ac992148a26c",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata = adata[list(set(adata.obs_names) & set(cell_data.index))].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "2dc795da-37eb-40b0-94cb-af86514ec059",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata.obs = cell_data.loc[adata.obs_names]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "58092300-5f5e-48d2-88bd-d695344d785c",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata.var[\"mt\"] = adata.var_names.str.startswith(\"MT-\")\n",
    "sc.pp.calculate_qc_metrics(\n",
    "    adata, qc_vars=[\"mt\"], percent_top=None, log1p=False, inplace=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d15a895-f920-4e6b-b474-d63b8ba8150d",
   "metadata": {},
   "source": [
    "### Data normalization\n",
    "\n",
    "It's important to save the **non normalized** and **non scaled** matrix in the raw slot!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "166f682f-fa31-450e-971b-7d7fc4d2a723",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lustre1/project/stg_00002/mambaforge/vsc33053/envs/scenicplus_development_tutorial/lib/python3.11/site-packages/scanpy/preprocessing/_simple.py:843: UserWarning: Revieved a view of an AnnData. Making a copy.\n",
      "  view_to_actual(adata)\n"
     ]
    }
   ],
   "source": [
    "adata.raw = adata\n",
    "sc.pp.normalize_total(adata, target_sum=1e4)\n",
    "sc.pp.log1p(adata)\n",
    "sc.pp.highly_variable_genes(adata, min_mean=0.0125, max_mean=3, min_disp=0.5)\n",
    "adata = adata[:, adata.var.highly_variable]\n",
    "sc.pp.scale(adata, max_value=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "79f79a00-96ce-426b-ac2a-a69bb37a7f3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VSN_cell_type</th>\n",
       "      <th>VSN_leiden_res0.3</th>\n",
       "      <th>VSN_leiden_res0.6</th>\n",
       "      <th>VSN_leiden_res0.9</th>\n",
       "      <th>VSN_leiden_res1.2</th>\n",
       "      <th>VSN_sample_id</th>\n",
       "      <th>Seurat_leiden_res0.6</th>\n",
       "      <th>Seurat_leiden_res1.2</th>\n",
       "      <th>Seurat_cell_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ACCTGTTGTGGATTCA-1</th>\n",
       "      <td>MOL_A</td>\n",
       "      <td>MOL_A (1)</td>\n",
       "      <td>MOL_A_2 (1)</td>\n",
       "      <td>MOL_A_1  (0)</td>\n",
       "      <td>MOL_A_2 (0)</td>\n",
       "      <td>10x_multiome_brain</td>\n",
       "      <td>MOL (0)</td>\n",
       "      <td>MOL (0)</td>\n",
       "      <td>MOL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CTAAAGCTCCCGCCTA-1</th>\n",
       "      <td>INH_VIP</td>\n",
       "      <td>INH_VIP (6)</td>\n",
       "      <td>INH_VIP (8)</td>\n",
       "      <td>INH_VIP (8)</td>\n",
       "      <td>INH_VIP (10)</td>\n",
       "      <td>10x_multiome_brain</td>\n",
       "      <td>INH_VIP (7)</td>\n",
       "      <td>INH_VIP (6)</td>\n",
       "      <td>INH_VIP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAGGAAGCACATAACT-1</th>\n",
       "      <td>AST_CER</td>\n",
       "      <td>AST_CER (2)</td>\n",
       "      <td>AST_CER (2)</td>\n",
       "      <td>AST_CER (2)</td>\n",
       "      <td>AST_CER_2 (5)</td>\n",
       "      <td>10x_multiome_brain</td>\n",
       "      <td>BG (2)</td>\n",
       "      <td>BG (2)</td>\n",
       "      <td>BG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GAGCATGCAATATGGA-1</th>\n",
       "      <td>MOL_A</td>\n",
       "      <td>MOL_A (1)</td>\n",
       "      <td>MOL_A_2 (1)</td>\n",
       "      <td>MOL_A_1  (0)</td>\n",
       "      <td>MOL_A_2 (0)</td>\n",
       "      <td>10x_multiome_brain</td>\n",
       "      <td>MOL (0)</td>\n",
       "      <td>MOL (0)</td>\n",
       "      <td>MOL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GCTCACAAGACAAGTG-1</th>\n",
       "      <td>MOL_A</td>\n",
       "      <td>MOL_A (1)</td>\n",
       "      <td>MOL_A_2 (1)</td>\n",
       "      <td>MOL_A_1  (0)</td>\n",
       "      <td>MOL_A_2 (0)</td>\n",
       "      <td>10x_multiome_brain</td>\n",
       "      <td>MOL (0)</td>\n",
       "      <td>MOL (1)</td>\n",
       "      <td>MOL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACATTGCAGCGGATTT-1</th>\n",
       "      <td>MOL_B</td>\n",
       "      <td>MOL_B (0)</td>\n",
       "      <td>MOL_B_1 (0)</td>\n",
       "      <td>MOL_B_1  (1)</td>\n",
       "      <td>MOL_B_3 (6)</td>\n",
       "      <td>10x_multiome_brain</td>\n",
       "      <td>NFOL (1)</td>\n",
       "      <td>NFOL (3)</td>\n",
       "      <td>NFOL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACGCCTTTCGACCTGA-1</th>\n",
       "      <td>AST</td>\n",
       "      <td>AST+ENDO (9)</td>\n",
       "      <td>AST+ENDO (6)</td>\n",
       "      <td>AST+ENDO (13)</td>\n",
       "      <td>AST (15)</td>\n",
       "      <td>10x_multiome_brain</td>\n",
       "      <td>AST+ENDO (6)</td>\n",
       "      <td>AST (7)</td>\n",
       "      <td>AST</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CTCAGGATCCACCTGT-1</th>\n",
       "      <td>MOL_B</td>\n",
       "      <td>MOL_B (0)</td>\n",
       "      <td>MOL_B_1 (0)</td>\n",
       "      <td>MOL_B_1  (1)</td>\n",
       "      <td>MOL_B_1 (1)</td>\n",
       "      <td>10x_multiome_brain</td>\n",
       "      <td>NFOL (1)</td>\n",
       "      <td>MOL (1)</td>\n",
       "      <td>MOL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CCAACATAGATAACCC-1</th>\n",
       "      <td>MOL_A</td>\n",
       "      <td>MOL_A (1)</td>\n",
       "      <td>MOL_A_1 (9)</td>\n",
       "      <td>MOL_A_1 (9)</td>\n",
       "      <td>MOL_A_1 (11)</td>\n",
       "      <td>10x_multiome_brain</td>\n",
       "      <td>NFOL (1)</td>\n",
       "      <td>COP (10)</td>\n",
       "      <td>COP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATGTAAGCACTTAGGC-1</th>\n",
       "      <td>MOL_B</td>\n",
       "      <td>MOL_B (0)</td>\n",
       "      <td>MOL_B_1 (0)</td>\n",
       "      <td>MOL_B_1  (1)</td>\n",
       "      <td>MOL_B_1 (1)</td>\n",
       "      <td>10x_multiome_brain</td>\n",
       "      <td>MOL (0)</td>\n",
       "      <td>MOL (0)</td>\n",
       "      <td>MOL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2313 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   VSN_cell_type VSN_leiden_res0.3 VSN_leiden_res0.6  \\\n",
       "ACCTGTTGTGGATTCA-1         MOL_A         MOL_A (1)       MOL_A_2 (1)   \n",
       "CTAAAGCTCCCGCCTA-1       INH_VIP       INH_VIP (6)       INH_VIP (8)   \n",
       "AAGGAAGCACATAACT-1       AST_CER       AST_CER (2)       AST_CER (2)   \n",
       "GAGCATGCAATATGGA-1         MOL_A         MOL_A (1)       MOL_A_2 (1)   \n",
       "GCTCACAAGACAAGTG-1         MOL_A         MOL_A (1)       MOL_A_2 (1)   \n",
       "...                          ...               ...               ...   \n",
       "ACATTGCAGCGGATTT-1         MOL_B         MOL_B (0)       MOL_B_1 (0)   \n",
       "ACGCCTTTCGACCTGA-1           AST      AST+ENDO (9)      AST+ENDO (6)   \n",
       "CTCAGGATCCACCTGT-1         MOL_B         MOL_B (0)       MOL_B_1 (0)   \n",
       "CCAACATAGATAACCC-1         MOL_A         MOL_A (1)       MOL_A_1 (9)   \n",
       "ATGTAAGCACTTAGGC-1         MOL_B         MOL_B (0)       MOL_B_1 (0)   \n",
       "\n",
       "                   VSN_leiden_res0.9 VSN_leiden_res1.2       VSN_sample_id  \\\n",
       "ACCTGTTGTGGATTCA-1      MOL_A_1  (0)       MOL_A_2 (0)  10x_multiome_brain   \n",
       "CTAAAGCTCCCGCCTA-1       INH_VIP (8)      INH_VIP (10)  10x_multiome_brain   \n",
       "AAGGAAGCACATAACT-1       AST_CER (2)     AST_CER_2 (5)  10x_multiome_brain   \n",
       "GAGCATGCAATATGGA-1      MOL_A_1  (0)       MOL_A_2 (0)  10x_multiome_brain   \n",
       "GCTCACAAGACAAGTG-1      MOL_A_1  (0)       MOL_A_2 (0)  10x_multiome_brain   \n",
       "...                              ...               ...                 ...   \n",
       "ACATTGCAGCGGATTT-1      MOL_B_1  (1)       MOL_B_3 (6)  10x_multiome_brain   \n",
       "ACGCCTTTCGACCTGA-1     AST+ENDO (13)          AST (15)  10x_multiome_brain   \n",
       "CTCAGGATCCACCTGT-1      MOL_B_1  (1)       MOL_B_1 (1)  10x_multiome_brain   \n",
       "CCAACATAGATAACCC-1       MOL_A_1 (9)      MOL_A_1 (11)  10x_multiome_brain   \n",
       "ATGTAAGCACTTAGGC-1      MOL_B_1  (1)       MOL_B_1 (1)  10x_multiome_brain   \n",
       "\n",
       "                   Seurat_leiden_res0.6 Seurat_leiden_res1.2 Seurat_cell_type  \n",
       "ACCTGTTGTGGATTCA-1              MOL (0)              MOL (0)              MOL  \n",
       "CTAAAGCTCCCGCCTA-1          INH_VIP (7)          INH_VIP (6)          INH_VIP  \n",
       "AAGGAAGCACATAACT-1               BG (2)               BG (2)               BG  \n",
       "GAGCATGCAATATGGA-1              MOL (0)              MOL (0)              MOL  \n",
       "GCTCACAAGACAAGTG-1              MOL (0)              MOL (1)              MOL  \n",
       "...                                 ...                  ...              ...  \n",
       "ACATTGCAGCGGATTT-1             NFOL (1)             NFOL (3)             NFOL  \n",
       "ACGCCTTTCGACCTGA-1         AST+ENDO (6)              AST (7)              AST  \n",
       "CTCAGGATCCACCTGT-1             NFOL (1)              MOL (1)              MOL  \n",
       "CCAACATAGATAACCC-1             NFOL (1)             COP (10)              COP  \n",
       "ATGTAAGCACTTAGGC-1              MOL (0)              MOL (0)              MOL  \n",
       "\n",
       "[2313 rows x 9 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.obs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "cabdd072-6cd1-4cc9-94bb-e24a284253cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lustre1/project/stg_00002/mambaforge/vsc33053/envs/scenicplus_development_tutorial/lib/python3.11/site-packages/scanpy/plotting/_tools/scatterplots.py:378: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
      "  cax = scatter(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sc.tl.pca(adata)\n",
    "sc.pl.pca(adata, color = \"Seurat_cell_type\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "3d177817-3fd5-430a-9c6a-e9760e139037",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.pp.neighbors(adata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "0487b3e9-0c82-4bbc-a682-8b320e6589d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.tl.umap(adata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "752524cc-d397-418b-9491-fce0aeae9e47",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lustre1/project/stg_00002/mambaforge/vsc33053/envs/scenicplus_development_tutorial/lib/python3.11/site-packages/scanpy/plotting/_tools/scatterplots.py:378: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
      "  cax = scatter(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sc.pl.umap(adata, color = \"Seurat_cell_type\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "df6560a0-92e6-4870-aed8-27f643dfc6b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata.write(\"adata.h5ad\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "scenicplus_development_tutorial",
   "language": "Python",
   "name": "scenicplus_development_tutorial"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
